There seems to be confusion in the marketplace about the term “AIOps” as far as what it means exactly, but there’s much less confusion about what it can do – Improve IT’s customer satisfaction scores by reducing noise, lowering call volume to the service desk, and slashing MTTR.
These are the types of benefits every IT organization is demanding, and the good news is they’re attainable right now.
Ayehu has partnered with Edge Technologies to show you a vision of what that looks like, and give you a glimpse at the promise that AIOps can bring to your IT organization.
Many of you have worked for years in the IT Operations and Systems Management space. Some of you may recall that in the mid-‘90s, Enterprise Systems Management and Business Service Management (or BSM for short) emerged as new disciplines that would bring together distributed systems and mainframes into a single pane of glass to solve problems. As you may know, Gartner killed off the BSM category in 2016 because vendors failed to deliver on these promised benefits.
In many large enterprises, the picture today still remains the same. Does this scene look familiar to you?
The CIO is still asking “why has customer experience dropped for our core service?. The IT Ops Manager is unsure what the cause might be as everything looks good thanks to fantastic ”monitoring”. And the SRE can’t make sense of any of the screens because he/she is suffering from information overload and isn’t sure where to look. No wonder MTTR is high!
Even with today’s AIOps vendors, and a market where new ones seem to be entering the space every week, the promise of universal views into your operations remains elusive. Nevertheless, it’s still a highly sought-after goal.
So the question is, what is preventing progress towards that goal?
Today, we still continue seeing knowledge and visibility silos across the enterprise from business units, support, operations, and engineering functions all the way through to 3rd-party service providers.
This is one of the main challenges to overcome if AIOps is going to succeed. Internal politics, tool proliferation, and un-integrated workflows continue contributing to the slow adoption of AIOps.
The promise of a “single pane of glass” never materialized leaving teams to use point products with limited integration and different data formats. The result? A huge and costly inventory of tools to manage and operate leading to more frustration.
It’s widely accepted across the industry that most monitoring dashboards today fail to provide required operational views that business needs.
AIOps aims to fully automate IT Operations workflows, but the reality today is that enterprises still struggle with tool sprawl resulting in the “swivel chair” effect. Your triage and remediation workflows are still very much reactive in nature, but the goal is to prevent incidents from happening in the first place as much as possible, right?
Also, in our experience over the years, the tools used today are more than likely to be replaced at some point, so the best approach is to have a vendor agnostic data visualization and integration solution for your dashboarding needs. The tools supplying the dashboard data feeds will come and go. Replacing them is a simple configuration change in Edge.
In order to break the knowledge and visibility silo challenges and create intelligent operations dashboards for increased AIOps adoption, think of the process in three parts:
Part 1: Integrate all required data sources ranging from customer experience and your enterprise IT domains to give business and service health views by role. For example, executive, manager, and analyst views.
Part 2: Integrate your existing event management, monitoring, and IT service management tools at the data and web layers to maximize your existing tool investments, skills, and standard operating procedures to become more proactive than ever before.
Part 3: Integrate your process automation tools (such as Ayehu) to create convenient and frictionless workflows that can be executed in either attended or unattended mode.
Now that we better understand the problems and obstacles in the way of making progress, let’s walk through the process of creating ideal intelligent operations dashboards for your AIOps initiatives by uniquely combining your data and tools into role-based views of your business and services.
When we think about digital transformation and the outcomes businesses are looking for, one of the goals CIOs have longed to achieve is ensuring that business and enterprise IT are completely aligned. This has been a goal for as long as most of us can remember!
To reflect that in our intelligent operations dashboards, let’s start from the top-level (see graphic below),which is a set of first-level business, customer, and end-user experience (EUE) dashboards that appeal to all levels of the organization.
The second level is a triage dashboard, designed to allow teams to quickly identify whether the server, network or application layer is the source of an outage or service health issue.
The third level is a dependency-mapping dashboard that links application, network, and server infrastructure together in topology views to understand the business impact.
The fourth level is individualized dashboards specifically designed for teams and dedicated roles — application, infrastructure, and network monitoring dashboards.This level of dashboard is where SMEs can directly access your existing best-in-class tools using Edge’s unique web UI proxying capability.
The fifth level gives you access to your raw data — including logs, events, packet traces, and call stack traces for example —so that detailed analysis can be performed in context to the issue being investigated.
By combining your data sources and tools into universal views using a single platform like Edge, you can provide appropriate dashboards to your executives, management, and SMEs that provide them access to the content they need and tasks they need to perform to be successful in their daily jobs.
By combining business and related service health metrics along with the power of integration with your data and tools, you can rapidly identify root cause, fix the problem for good, and slash your key performance indicators such as MTTR. Many Edge customers report having happier customers, greater alignment between business and IT, eradication of visibility silos, and overall better decision making and outcomes from their deployment.
Not least of all, their most valuable assets (people) are more successful in meeting their goals and performing their job tasks.
Now let’s talk a bit about automation.
Digital Transformation is a buzzword you hear a lot about these days. It doesn’t have one standard definition but can basically be understood to mean the collection of technology, process, and even cultural disruptions an organization adopts to maximize its competitiveness in the 4th industrial revolution.
Those technology disruptions can include things like cloud computing, artificial intelligence, chatbots, and of course automation.
The process disruptions include things like Agile or Six Sigma, and a cultural disruption might be something like repositioning the organization’s focus to be better aligned with the customer journey.
For IT departments, digital transformation ultimately boils down to optimizing and accelerating delivery of computing services, regardless of whether the customer is external or internal.
When it comes to incident monitoring, one thing an IT department can do as part of its digital transformation, is to consolidate the visualization of all their various monitoring tools into a single pane of glass, as Edge Technologies enables. A unified dashboard providing a 360° view of operations, can also provide an extraordinary opportunity to not only centralize incident monitoring but also to automate incident remediation. That represents a big step forward in the digital transformation of data centers, and a perfect example of how 1 plus 1 can sometimes equal 3.
A recent paper published by Gartner (ID G00390283 – October 9, 2019) advised its readers that an ideal performance monitoring dashboard framework must aim to “Provide for the rapid triage and remediation of performance issues…”.
No argument there. Ayehu and Edge Technologies agree that combining automation with performance monitoring is central to an ideal dashboard framework. But perhaps the most important word to emphasize in Gartner’s recommendation is “rapid”.
Unfortunately, “rapid” is not an adjective that the vast majority of service desks can use to describe their MTTR today.
MetricNet, the IT consulting firm that publishes benchmarks, performance metrics, and scorecards for a variety of IT-related activities, claims that the average incident MTTR is 8.40 business hours. If you’re an end user in an organization who just submitted a ticket to the help desk, you do NOT want to hear that it will take an average of 8.40 business hours to remediate your issue. On the contrary, you want to know that your IT department is doing everything it can to expedite a resolution for your incident, before it starts hampering your personal productivity.
When it comes to MTTR, your mileage may vary of course, depending on your IT organization’s ticket backlog, user population density, and complexity of tickets handled.
Regardless though, one universal factor that’s slowing down almost all IT organizations is the ever-increasing user demand for IT services, which often leads to growing system complexity in your environment to accommodate that growth, and ultimately results in ever increasing pressure on your staff to keep up.
However, people don’t scale very well. Even the very best data center workers can only do so much. At some point, and that point is pretty much right now, automation has got to do more and more of the repetitive, tedious, laborious tasks all this growth in demand for services and increased system complexity is creating.
That’s why consolidating visualization of all your monitoring tools into a single pane of glass and incorporating automated incident remediation into that dashboard, can give your IT department the critical boost it needs to overcome the lack of human scalability.
If you’re interested in test driving Ayehu NG v1.6 with all its cool new features, download your very own free 30-day trial version from the link below:
Automation has been at the forefront of the digital revolution for decades, primarily because it maximizes efficiency, reduces costs and accelerates service levels. But the cloud, mobile and other innovative technologies – coupled with an ever-growing volume of raw data – have led to dramatically more complex IT environments.
According to ESG’s IT Spending Intentions Survey from 2018, 68% of those surveyed said their IT infrastructures are significantly more complex than they were just two years ago. Furthermore, 39% of respondents listed automated IT operations as a critical component of survival in today’s digital age.
In response to this increasing complexity, organizations are beginning to make the shift toward the next generation of automation – from basic to intelligent. This new level of automation involves technologies like machine learning and artificial intelligence to orchestrate workflows across a multitude of tools, systems and processes.
In fact, with the right platform, it is now possible to fully automate L2 and L3 tasks – functions which have traditionally required the use of human judgment. Now, those insights lie within the data itself and can be extracted, interpreted and leveraged autonomously by AI.
Embracing intelligent process automation is also enabling enterprises to lay the foundation for AIOps, a focus area that experts predict will boom over the next five years or so.
AI and ML: Augmenting IT Operations
AIOps is helping IT teams manage the increasing challenges created by data and digital disruption, leveraging intelligent process automation and orchestration to gain competitive advantage. Thanks to the powerful processing capabilities of artificial intelligence, IT can sort through mind-boggling amounts of data points to find the proverbial needle in a haystack.
The role of humans in this increasingly tech-driven environment is still present, though it too is evolving. Rather than relying on error-prone employees to handle the bulk of the processing work, human cognition and advanced skillsets are being used to define that proverbial needle.
In response to this, more organizations are focusing their efforts on reskilling and upskilling their existing staff to bring them up to speed on ML and AI technologies.
Making the Switch to Autonomous Operations
Autonomous operations (AO) utilizes advanced AI to deliver unassisted responses to IT incidents across the entire infrastructure. Thanks to the self-learning capabilities of ML algorithms, AO is able to continuously improve its ability to identify patterns and carry out the appropriate actions.
Again, human workers are still needed in an AO-driven environment, but in the role of supervisor as opposed to operator. Yet as the software continues to evolve and improve, and as errors consistently decrease over time, full autonomy and a zero-touch IT operations environment will one day become a very real possibility.
The Role of Data
The key to success with intelligent automation is accurate data, as this enables users to write more impactful rules. There is little to no value in static data. These days, it’s all about dynamic information which comes from things like descriptive metadata as well as relational and behavioral data.
In order to harness this dynamic data and gain adequate insights from it, organizations need to develop software-defined IT environments. Intelligent process automation is about the ability to not only proactively identify anomalies, but to also remediate those issues automatically without causing any business disruption.
The Right Way to Automate Intelligently
In today’s competitive landscape, automation is no longer an option but a necessity. That said, there’s a right way and a wrong way to leverage this game-changing technology. Start by weighing the time, effort, complexity and frequency of a given task and then benchmarking these factors against the cost of transitioning that task to intelligent process automation. From there, create a prioritized list. This will help you maximize ROI and harness the full potential of intelligent IT operations.
Not sure where to start? Why not give intelligent process automation a test drive free for 30 full days? Click here to launch your Ayehu trial today.
In today’s increasingly complex digital environment, the ability to pinpoint, resolve and mitigate potential IT problems has never been more critical. And with a hybrid blend of public and private cloud, on-premises and virtual servers, a growing variety of mobile devices and a skyrocketing volume of network and application traffic, it’s also never been more challenging. To address this significant concern, organizations are turning to artificial intelligence for IT operations – or AIOps for short.
The term AIOps encompasses the use of advanced data analytics technologies, such as AI and machine learning, to automate the process of identifying and remediating performance issues. AIOps leverages the colossal volume of data generated by IT services and systems to proactively monitor the infrastructure and gain complete visibility over all system and application dependencies. These advanced capabilities enable AIOps to manage and address potential problems, often before they occur.
Organizations put AIOps in place to gather and analyze all IT operational data and simultaneously automate all main IT operations. The AIOps system then organizes and prioritizes that data, presenting it to IT managers so they can react accordingly. In short, AIOps provides IT decision-makers with the insight they need to stay a step ahead of IT operations. Gartner predicts that by 2023, the use of AIOps will increase from 5% to 30%.
The Key is Automation
The most critical component to a smooth and efficiently run AIOps is automation. This technology helps AIOps to perform ongoing monitoring while adhering to predetermined policies and dependency mapping and quickly and effectively carry out the steps necessary to resolve events or failures.
With all of these technologies operating in tandem, and automation at the center, AIOps can ultimately help to reduce the volume of potentially damaging events, provide proactive alerts to issues that could cause an outage, pinpoint the root cause of those issues and apply intelligent process automation to autonomously remediate.
AIOps is capable of increasing the effectiveness of infrastructure resources, streamlining and expediting service requests and problem resolution, and ultimately generating consistent, measurable value from its ability to support current and future business initiatives.
The Benefits of AIOps
Harnessing the power of automation in combination with AIOps delivers a multitude of benefits for IT. Firstly, it can dramatically enhance and improve the effectiveness of existing tools and services. And since it saves time while also increasing efficiency and productivity, organizations employing AIOps can also realize a decrease in overall expenditure.
Likewise, AIOps can also reduce the amount of time and effort currently required to manage service requests and remediate performance issues and outages. All of this adds up to improved service levels, a significant reduction in risk, and a quicker time-to-market for new initiatives.
Automated AIOps runs on a 3-phrase approach:
In other words, it monitors the environment to detect any potential anomalies or concerns, then analyzes, validates and prioritizes those potential events before finally determining the best course of action to take to address the issue at hand. While this last step may involve escalation to a human decision-maker, in most cases, these steps can all be carried out without the need for human intervention. Therein lies the true value of AIOps.
To learn firsthand how AIOps can help position your organization for future stability and sustainable success, try it yourself for 30 days. Click here to start your full-feature trial of Ayehu NG today.
The ability to proactively predict and remediate IT incidents BEFORE they occur, rather than react to them after they’ve already happened, is one of the key value propositions of a new IT operations category called AIOps, which stands for Artificial Intelligence for IT Operations.
Leveraging the AI part of AIOps to mitigate problems before they become problems is a game changer for IT. So we’ve partnered with Loom Systems, who like ourselves are a Gartner Cool Vendor in their category, to demonstrate how two best-of-breed providers can integrate their respective platforms to create an enterprise-grade AIOps solution. In doing so, we believe the result is an early glimpse at the self-healing data center of tomorrow, and we think you’ll be intrigued to experience how you can peek over the horizon to see and automatically remediate incidents before they impact end-users.
Let’s start with the obvious question many of you might have on your mind – what is AIOps? It is after all, a term that kind of snuck up on all of us.
The term AIOps, like a lot of buzzwords in our industry, was originated by Gartner. In this case, a Sr. Director Analyst named Colin Fletcher coined it in 2016, and its earliest published appearance (as best I can tell) was in early 2017.
Interestingly though, Colin told me he originally meant the term to refer to Algorithmic IT Operations.
Since then it’s evolved to refer to Artificial Intelligence for IT Operations.
Now we all know how it is in IT marketing. New buzzwords are used to refresh a category and create excitement. So is AIOps basically just a recycling of the term “IT monitoring”? Are IT monitoring and AIOps basically the same? Twins, so to speak, but with different names?
Here’s the definition for IT Monitoring, courtesy of an internet publication many of you are probably aware of called TechTarget:
|“IT monitoring is the process to gather metrics about the operations of an IT environment’s hardware and software to ensure everything functions as expected to support applications and services. Basic monitoring is performed through device operation checks, while more advanced monitoring gives granular views on operational statuses, including average response times, number of application instances, error and request rates, CPU usage and application availability.”|
The operative words there are “gather metrics” – “through device operation checks”.
This reflects one of the primary characteristics of IT Monitoring – namely that it’s passive in nature.
And here’s Colin Fletcher’s original definition for AIOps:
“AIOps platforms utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.”
Unlike IT Monitoring, AIOps is proactive and far more sophisticated. So AIOps is a LOT MORE than just IT Monitoring.
At this point you may be asking yourself, “OK, but how can this benefit me?”
As we all know, in today’s Digital Era, most businesses are digital or undergoing a digital transformation, which means that IT systems are replacing many traditional physical business processes, and that in turn means more work for IT Operations.
In fact, IT Operations engineers have become responsible for the customers’ digital experience. When your organization’s systems are misbehaving, underperforming, or worse not working at all, your customers’ satisfaction is affected, which often leads to customer churn.
It’s that simple.
End users often use applications or websites and love how simple and intuitive they can be. In IT though, we all know that building something to look nice and simple, can actually be quite difficult. That’s because there are usually many technologies under the hood that need to work together seamlessly in order for these digital experiences to run smoothly.
As if that wasn’t enough, let’s add some more complexity:
With Cloud Computing on the one hand, and Microservices architectures on the other, things become even more complex, for the following reasons:
- Cloud computing means abstraction – that can lead to struggles understanding what the impact of a performance issue on a host will do to other components of your applications.
- These environments change dynamically, making it harder to stay on top of everything.
- Microservices often require disparate data sources, each generating its own logs and metrics, making tracing and correlation an inherent part of root cause analysis (RCA).
So, the increased complexity of digital businesses architectures, coupled with the explosion of different data types, and the elevated expectations consumers have these days for seamless end user experiences, makes the life of IT Operations teams quite challenging.
AIOps is a set of tools that enable achievement of optimum availability and performance by leveraging machine learning technologies against massive data stores with wide variance. The big idea here is to use machines to deal with machines.
Here are some examples of the challenges customers often look to address by implementing AIOps:
- Outage prevention – organizations in the process of cloud migration or architecture change, often look for modern technologies like AIOps to help them prevent outages before the business is affected. This is a marked difference from 2 years ago when the market was just focused on noise reduction. Artificial intelligence and machine learning have raised expectations of how much more is possible.
- Capturing different data feeds – this means it’s not just about alerts anymore. There’s a huge need to consolidate logs, metrics, and events together, and to make sense out of them as a whole.
- Consolidation of tools – this one is mainly about the workflow of the users. They’d like AIOps to make their daily lives easier and consolidate everything into one system.
A monitoring architecture for modern enterprises that can do all of the above would be a real-life example of a self-healing architecture.
Everything starts with observability. Many enterprises use one or more infrastructure monitoring tools. Application Performance Management (APM) monitors do a great job in monitoring performance, but are very limited for the application stack and log management, rendering them a bit unhelpful for triage and forensic investigations.
These monitoring tools are usually focused on specific data feeds or IT layers, and they emit alerts when things go wrong. However, these can lead to confusing alert storms.
This is another reason why organizations are beginning to leverage AIOps to work for them and make sense out of it all. Think of AIOps as a robot that turns monotonous data into information you cannot ignore. In our case, turning logs into predictions or early stage detection of an outage.
Now that you know something is about to break, can you prevent it from happening? That’s exactly the idea of self-healing. When working with an intelligent automation platform like Ayehu, you can build simple (or complex) remediation workflows, that can take the alert from Loom Systems and automatically remediate the incident BEFORE it becomes something more calamitous.
In your monitoring architecture, you want the Automation tool to seamlessly interact with both the AIOps solution and your ITSM platform, to open a ticket and update it as you’re taking remedial action.
When configured properly, this architecture can resolve issues before they affect the business, while also documenting what happened for future reference.
Gartner concurs with this approach.
In a paper published earlier this year (ID G00384249 – April 24, 2019), they wrote that:
|“AI technologies play an important role in I andO, providing benefits such as reduced mean time to response (MTTR), faster root cause analysis (RCA) and increased I andO productivity. AI technologies enable I andO teams to minimize low-value repetitive tasks and engage in higher-productivity/value-oriented actions.”|
No ambiguity there.
A little further down in the same paper, Gartner gave the following recommended actions, representing their most current advice to infrastructure and operations leaders regarding AIOps and automation:
|“Embark on a journey toward driving intelligent automation. This involves managing and driving AI capabilities that are embedded by infrastructure vendors, in addition to reusing artificial intelligence for operations (AIOps) capabilities to drive end-to-end (from digital product to infrastructure) automation.”|
With AIOps + Automation, it’s possible to predict and prevent network outages or other major disruptions by proactively detecting the conditions leading up to them and automatically remediating them BEFORE disaster strikes. Given how costly a service interruption can be to an enterprise, avoiding issues before they happen will be a critical function in the self-healing data center of tomorrow.
Digital transformation has simultaneously simplified and added a layer of complexity to the modern world of IT operations. Managing multiple environments across a number of locations invoked the need to introduce several disparate tools and platforms, leaving IT siloed and, oftentimes, overwhelmed. This has perpetuated the need for artificial intelligence for IT operations, or AIOps for short. For those not yet leveraging AIOps, or who are still in the beginning stages, here are three real-world, value-added use cases to consider.
Threat Detection – AIOps is the perfect complement to a security management strategy because its machine learning algorithms are capable of mining massive amounts of data for scripts, botnets and other threats or anomalies that could potentially harm a network. This is especially true for threats that are complex and sophisticated, which is why it’s such a valuable addition.
Intelligent Alerting – Today’s ITOps teams are being inundated with alerts of which only a small portion are actually critical. AIOps can manage these alerts autonomously, evaluating, identifying core issues, prioritizing and either escalating or remediating them without the need for human intervention. Imagine trimming that overflowing inbox of alerts down to just one or two that truly matter.
Capacity Optimization – Through the use of AI-based statistical analysis, IT operations teams can optimize application workloads and availability across the entire infrastructure. This technology is capable of proactively monitoring bandwidth, utilization, CPU, memory and much more, with the goal of maximizing application uptime. AIOps can also be used for predictive capacity planning.
Of course, this is really just the beginning. As environments become increasingly complex and technology options continue to grow, IT operations teams will find themselves under even more pressure to deliver maximum business value with minimal downtime. AIOps emerges as the ideal solution, facilitating infrastructure monitoring and management that is much faster and far more efficient. It’s no surprise, that IT leaders and other key decision-makers are starting to take notice.
Today, AIOps is all about threat management, streamlined alerting and maximizing uptime. Tomorrow, IT automation powered by artificial intelligence, machine learning and natural language processing technology is positioned to forge entirely new pathways for innovation and growth. In other words, the journey has just begun and the future is beaming with possibility.
Want to get in on the ground floor? Grab your free 30-day trial of Ayehu NG and put the power of AIOps to work for your organization.
Today’s IT teams are dealing with a growing mountain of data. What’s more, they’re finding themselves having to use a multitude of tools in order to monitor and manage that data. In situations of technical outages, this can make it incredibly difficult and time-intensive to identify and resolve underlying issues. Anyone in business knows that even just a tiny amount of down-time can have a serious and costly impact on the bottom line. And it’s the IT team that bears the brunt of the burden.
Take, for example, the two largest supermarket chains in Australia. Last year, both experienced severe technical issues which forced them to shut down several stores while they worked on fixing the problem. Not only did those companies lose revenue during the shutdown, but they also suffered a serious blow to their reputation. In other words, customers were not happy.
To better and more quickly identify, resolve and prevent outages and other problems, organizations are turning to artificial intelligence for IT operations (AIOps) – the long-term impact of which will be nothing short of transformational.
What is AIOps
In simplest of terms, AIOps combines data science and machine learning functionality to enhance and/or replace the majority of IT operations functions. This includes performance and availability monitoring, event analysis and correlation, ITSM and automation. To put it even more simply, AIOps platforms gather and analyze all of the data produced by IT to extract what’s of value and present meaningful insights.
How to Get Started with AIOps
Step 1: Don’t put it on the back burner.
If you really want to reap the benefits of AI for your IT operations, the time to jump on the AIOps bandwagon is now. Don’t make this an afterthought or push it out as some far-off future initiative. Even if the actual deployment isn’t imminent, start preparing yourself and others within your organization by becoming familiar with artificial intelligence and machine learning capabilities today. This way, in the event that priorities shift and you need to implement sooner, you’ll already be a few steps ahead of the game.
Step 2: Be careful when choosing your initial test case.
The concept of AIOps at scale may seem overwhelming, but keep in mind that truly transformative initiatives almost always start small. Focus first on capturing knowledge, testing frequently and iterating as needed. You don’t need to be an expert right out of the gate, and not every project you spearhead will be a resounding success. Just be mindful of what you’re starting with and work your way up from there.
Step 3: Work on developing and demonstrating your proficiency.
If you are leading the AIOps charge in your organization, you’ll inevitably be the go-to subject matter expert, at least initially. It will be up to you to communicate and convey the value of the technology to your colleagues and others in leadership. Wear your role with pride and start assembling a team of others who can champion the cause alongside you. Start by identifying gaps that exist in skills and experience, and then create a plan to address those gaps together.
Step 4: Don’t be afraid to experiment.
There are already many AIOps platforms on the market that are incredibly complex and subsequently cost-prohibitive. As with any tech product or solution, it’s wise do experiment and test the waters. Keep in mind that more features doesn’t necessarily equate to a better product. Your organization may not need all those bells and whistles. If possible, take advantage of product demos and free trials. This will enable you to evaluate AIOps uses and applications specific to your business needs without having to invest too heavily or commit to one particular solution.
Step 5: Expand your vision beyond the IT department.
Data management is a massive component of AIOps. Take a step back and examine your organization. Chances are very high that your existing teams are already skilled in this area and that there are data and analytics tools already present within your organization. Resist the urge to reinvent the wheel and be willing to expand your vision to look beyond the IT department. It could save you tremendous time, effort and money.
Step 6: Standardize whenever possible and modernize wherever it makes sense.
You can prepare your existing infrastructure so that it is capable of supporting an AIOps implementation in the future by developing a consistent automation architecture, immutable infrastructure patterns and infrastructure as code (IaC).
Step 7: Consider build-vs-buy.
Understand that there are a number of variables involved in making a shift to AIOps. Likewise, the platforms available on the market today will continue to evolve, as will the infrastructure and applications for which you are responsible currently. Be mindful of this as you weigh whether to purchase a solution or build one of your own. Ideally, the best answer will likely be a combination of the two, so be prepared to figure out which approach best applies where and by how much.
Over the past few years, AIOps has developed from an emerging category to an IT necessity. Successful companies are beginning to leverage AIOps to automate and improve IT operations by applying machine learning to their data. Furthermore, forward-thinking organizations will use AIOps to draw valuable insights from their IT data that will help drive strategic business decisions.
If AIOps is on your to-do list (and it certainly should be), the steps outlined above should help you to, at the very least, lay the groundwork so that when the time comes to implement, the process will go faster and much more smoothly.
Why wait? Experience the next generation of IT automation, powered by machine learning and artificial intelligence and get started on the fast track to successful AIOps deployment. Start your free 30 day trial of Ayehu today!
In today’s volatile marketplace, businesses in every industry are focusing on cutting costs. Unfortunately, some folks still view IT as an expense and an area in which the metaphorical belt can be tightened. What they don’t realize, and what an increasing number of CIO’s are embracing, is that implementing AIOps can actually result in reduced expenditure overall.
CIO’s that are concentrating on IT as a force of operational automation, integration and control are losing ground to executives who see technology as a business amplifier and a source of innovation. Ongoing advances in technology are now providing forward-thinking CIO’s with a much broader spectrum with which to work in terms of cutting costs across the entire organizational platform.
It has nothing to do with cutting IT capability, but rather finding ways to make IT operations more efficient. This is primarily achieved through intelligent automation, which significantly reduces the time and resources needed to run both routine tasks as well as complex workflows. When these tasks and workflows are automated, IT personnel are freed up to focus on other, more critical matters, thereby improving the overall performance of the department and subsequently the company as a whole.
Another way that CIO’s are leveraging AIOps for the benefit of their organizations is through improvement of incident management and mean time to resolution (MTTR). Critical system errors are costly and can have a significant impact on an organization’s bottom line. AI-powered intelligent automation is allowing businesses to manage incidents and downtime scenarios more efficiently and in a much timelier manner, which means less risk of negative impact, both on the business and on the end user.
AIOps isn’t just becoming a tool for cutting costs, either. It’s also significantly improving business performance, which plays a key role in increasing revenue. According to a recent survey conducted by Gartner, the main focus of CIO’s in the current climate is growth. They want to attract new customers and effectively retain their current ones. Intelligent automation helps to improve service levels, thereby improving the customer experience.
In a time when budgets are at the forefront of every manager’s mind, from the top down to those on the front line, finding areas to improve service and lower expenditure has become a necessity. The concept of AIOps has opened up a number of opportunities for streamlining operations and improving efficiency, which ultimately achieves the goal of reducing costs and boosting enterprise growth. By applying technology as an amplifier to business operations, rather than as simply an individual component, organizations that are embracing artificial intelligence and automation are already reaping the benefits and are poised for ongoing success as we move toward the future.
Ready to join these forward-thinking business leaders? Download your free trial of Ayehu and start building your AIOps strategy today.
Thanks to the forces of digital transformation, IT operations is undergoing some pretty significant changes. Traditional IT management techniques are becoming obsolete and an entire restructuring of our IT ecosystems is underway. In response, IT operations leaders are using artificial intelligence to help them do their work better, faster and cheaper. Gartner has coined a term for this fundamental shift. It’s known as Artificial Intelligence for IT Operations, or AIOps for short.
AIOps addresses the challenges of speed, scale and complexity that IT leaders are facing in the wake of digital transformation. Here are five specific factors that are driving forces behind AIOps.
Manual Infrastructure Management – Today’s IT environments are a mishmash of SaaS integrations, third party services, mobile, managed and unmanaged cloud and more. Traditional infrastructure management approaches, like manual tracking and oversight, are simply not adequate in these dynamic, ever-changing environments.
Increase in Data Retention Requirements – The volume of events and alerts being generated through performance monitoring is growing at an exponential rate. Furthermore, the growing number of APIs, IOT devices, mobile applications and digital and/or machine users is driving service ticket volumes through the roof. This has made manual analysis and reporting far too complex and cumbersome.
Demand for Faster Response Time – The more enterprises digitize their business, the more quickly infrastructure problems must be addressed. User expectations have evolved thanks to the consumerization of technology, which is driving the demand for faster reactions to IT events (whether actual or perceived). This is compounded when the issue in question affects user experience.
More/Expanding Computing Power – Given how easy it has become to adopt cloud infrastructure and third party services has empowered individual lines of business (LOB) to develop their own IT applications and solutions. As a result, both budget and control have moved from the center of IT to the very edges of the network, driving the rollout of more computing power.
Influence and Power of Developers – In modern DevOps, programmers are taking more responsibility for monitoring at the application level, however, responsibility for the interaction between services, applications and infrastructure, as well as accountability for the overall health and function of the IT ecosystem still lies at the feet of core IT. As digital businesses are becoming more complex, IT Ops is taking on more responsibility.
Digital transformation is something organizations in every industry and across the entire globe are striving for. AIOps could very well hold the key to success. Power your AIOps with the right solution. Click here to download your free 30-day trial of Ayehu today.
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